Welcome!¶

This is an interactive Python notebook that has been made by Washieu Anan and For my project, I wanted to inform others on ways to improve their recycling habits. In this notebook, you will see my statistical analysis of the dataset that we retrieved from NYC OpenData. In this dataset, I was provided with the specific zones that garbage was collected in, the specific districts in which garbage was collected in, the fiscal month in which garbage was collected in, the fiscal year in which garbage was collected in, the total diversion rate of the garbage (per zone and district) and the capture rate of paper that was officially recycled (per zone and district).

In order to understand the purpose of each section of code, please refer to the green lines of text above each section of code. The green lines will help you with understanding each section of our code.

#Below in our first cell, I have imported many crucial Python libraries that are needed to best statistically analyze the data that I have.
import pandas as pd
import numpy as np
import random
import math
import itertools
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
import plotly.graph_objects as go
import plotly.express as px
pd.set_option('display.max_rows', None)

#If you are using Google Colaboratory, please utilize this following cell to upload your .csv files onto your notebooks. 
from google.colab import files
uploaded = files.upload()

Saving Recycling_Diversion_and_Capture_Rates.csv to Recycling_Diversion_and_Capture_Rates.csv

#This following cell tells the computer to take the dataset into the computer and store it as variable. 
df = pd.read_csv("Recycling_Diversion_and_Capture_Rates.csv")

#I did not necessairly have to use the .head() function. We merely wanted to test whether or not our dataset was officially inputted into the system. 
df.head()

#In this cell, I am listing all of the columns within the dataset, for which we have already done in our inductory segment. 
df.columns

Index(['Zone', 'District', 'Fiscal Month Number', 'Fiscal Year', 'Month Name',
       'Diversion Rate-Total (Total Recycling / Total Waste)',
       'Capture Rate-Paper (Total Paper / Max Paper)',
       'Capture Rate-MGP (Total MGP / Max MGP)',
       'Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100'],
      dtype='object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2832 entries, 0 to 2831
Data columns (total 9 columns):
 #   Column                                                                                   Non-Null Count  Dtype  
---  ------                                                                                   --------------  -----  
 0   Zone                                                                                     2832 non-null   object 
 1   District                                                                                 2832 non-null   object 
 2   Fiscal Month Number                                                                      2832 non-null   int64  
 3   Fiscal Year                                                                              2832 non-null   int64  
 4   Month Name                                                                               2832 non-null   object 
 5   Diversion Rate-Total (Total Recycling / Total Waste)                                     2832 non-null   float64
 6   Capture Rate-Paper (Total Paper / Max Paper)                                             2832 non-null   float64
 7   Capture Rate-MGP (Total MGP / Max MGP)                                                   2832 non-null   float64
 8   Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100  2832 non-null   float64
dtypes: float64(4), int64(2), object(3)
memory usage: 199.2+ KB

#In this cell, I am telling the computer to read each columns as a string of letters rather than a number that has been located within an array. 
df['Fiscal_Date'] = df['Fiscal Year'].astype(str) + '-' + df['Fiscal Month Number'].astype(str)
df['Fiscal_Date'] = pd.to_datetime(df['Fiscal_Date'])
df['Normal_Date'] = df['Fiscal Year'].astype(str) + '-' + df['Month Name']
df['Normal_Date'] = pd.to_datetime(df['Normal_Date'])

#In this cell, I outline the specific zones that are available. 
df.Zone.unique()

array(['Brooklyn North', 'Brooklyn South', 'Bronx', 'Manhattan',
       'Queens East', 'Queens West', 'Staten Island'], dtype=object)

#In this cell, I am renaming each of the zones to look much cleaner in appearance when being shown to the public. 
df_brooklyn_north = df[df['Zone'] == 'Brooklyn North']
df_brooklyn_south = df[df['Zone'] == 'Brooklyn South']
df_bronx = df[df['Zone'] == 'Bronx']
df_manhattan = df[df['Zone'] == 'Manhattan']
df_queens_east = df[df['Zone'] == 'Queens East']
df_queens_west = df[df['Zone'] == 'Queens West']
df_staten_island = df[df['Zone'] == 'Staten Island']

#In this cell, I am creating a list in which we list all of the specific zones. 
zone_list = [df_brooklyn_north, df_brooklyn_south, df_bronx, df_manhattan, df_queens_east, df_queens_west, df_staten_island]

#In this cell, I am telling the computer to go through each zone. While going through each zone, 
#I am telling the commputer to define the shape (the number of rows) and the name of the zone that corresponds with it. 
for df_zone in zone_list:
    shape = df_zone.shape
    zone_name = df_zone.Zone.unique()[0]
    print(zone_name, shape[0])

Brooklyn North 432
Brooklyn South 432
Bronx 576
Manhattan 576
Queens East 336
Queens West 336
Staten Island 144

#In this cell, I am telling the computer to tell us the number of districts within this dataset. 
districts = []
for zone in df.Zone.unique():
    zone_df = df[df['Zone'] == zone]
    count = 0
    for district in zone_df.District.unique():
      districts.append(district)
      
len(districts)

59

#In this cell, I am telling the computer to go through each zone and district and create a "scatter plot" that contains a line for the:
#Total Diversion Rate, Total Capture Rate of Paper, Total Capture Rate of Metal, Glass, and Plastic, Overall Capture Rate. 
#If you hover over a line, it will provide you with the specific name of the line that you are hovering over. 
for zone in df.Zone.unique():
    zone_df = df[df['Zone'] == zone]
    count = 0
    for district in zone_df.District.unique():
        district_df = zone_df[zone_df['District'] == district]
        district_df = district_df.sort_values(by=['Normal_Date'])
        fig = go.Figure()
        fig.add_trace(go.Scatter(
                        x=district_df.Normal_Date,
                        y=district_df['Diversion Rate-Total (Total Recycling / Total Waste)'],
                        name=f"'Diversion Rate-Total' - {district_df.District.unique()[0]}",
                        line_color='deepskyblue',
                        opacity=0.8))

        fig.add_trace(go.Scatter(
                        x=district_df.Normal_Date,
                        y=district_df['Capture Rate-Paper (Total Paper / Max Paper)'],
                        name=f"'Capture Rate-Paper' - {district_df.District.unique()[0]}",
                        line_color='firebrick',
                        opacity=0.8))

        fig.add_trace(go.Scatter(
                        x=district_df.Normal_Date,
                        y=district_df['Capture Rate-MGP (Total MGP / Max MGP)'],
                        name=f"'Capture Rate-MGP' - {district_df.District.unique()[0]}",
                        line_color='green',
                        opacity=0.8))
        
        fig.add_trace(go.Scatter(
                        x=district_df.Normal_Date,
                        y=district_df['Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100'],
                        name=f"'Capture Rate-Total' - {district_df.District.unique()[0]}",
                        line_color='rgb(102,102,255)',
                        opacity=0.8))

        fig.update_layout(xaxis_range=[min(district_df.Normal_Date),max(district_df.Normal_Date)],
                          title_text=f"'Rates for District ' + {district_df.District.unique()[0]}",xaxis_title="Date",
            yaxis_title="Rate",
                        xaxis_rangeslider_visible=True)
        fig.show()

#In this cell, I am finding the mean, standard deviation, minimum, maximum, and percentages of the data to better analyze the type of data that I am looking at. 
for zone in df.Zone.unique():
    zone_df = df[df['Zone'] == zone]
    count = 0
    for district in zone_df.District.unique():
        district_df = zone_df[zone_df['District'] == district]
        district_df = district_df.sort_values(by=['Normal_Date'])
        print(district + '  statistics')
        display(district_df.describe())
        print('\n\n\n')

BKN01  statistics



BKN02  statistics



BKN03  statistics



BKN04  statistics



BKN05  statistics



BKN08  statistics



BKN09  statistics



BKN16  statistics



BKN17  statistics



BKS06  statistics

Analysis¶

Using the graphs and charts that have been developed via our computer, we can now begin to analyze our data.

A majority of the Browning community lives in the Upper East Side. The Upper East Side is considered District 08 in Manhattan. Below, the graph created for the Upper East Side will be shown: alt text

Looking at this graph, we can understand that the Diversion Rate (for the Upper East Side) is considerably low. Less than 30% of all materials used in the Upper East Side is recycled. Such practices lead to an overall increase in pollution. By not recycling the materials that we use, we are exposing ourselves and other forms of life to harmful chemicals and particles that can pose a threat to our overall well-being. We are also overfilling our landfills and creating the destruction of our natural habitats.

Many members of the Browning community also live in the Upper West Side. The Upper West Side is considered District 10 in Manhattan. Below, the graph created for the Upper West Wide will be shown: alt text

Looking at this graph, we can see many similarties to the graph that was generated for the Upper East Side. The Diversion Rate Total is also considerably low with the Total Diversion Rate being less than 30%.

Also, a common trend between both of the graphs is the relative time of spikes within the graph. Those spikes are often around the summer and early winter. Such spikes are understandable because of the common influx of tourists coming into New York City. Such tourists may not know of the certain ways in which they can deposit or manage the trash that they generate.

Using the information that we can see, here are some ways to make sure that our Total Diversion Rate increase.

Know what you can and cannot recycle:
- New York has provided people with a complete list that outlines the specific rules and regulations on how to recycle a certain item (Recycling Info)
- Many items have a symbol that implies whether an item is recyclable or not (♻️)
Begin to purchase more recyceable items.
- The world around us is surrounded with technology. However, as more people begin to use technology, technological waste also begins to increase.
  - If you have an old device that you do not use anymore, many companies have programs in which they will purchase your device from you and recycle it. Not only do you make extra money but you are also saving the world! Apple also utilizes many components of recycled phones (metal and glass) to utilize them when building new phones.
  - Begin to purchase rechargeable batteries. Rechargeable batteries allow for an overall decrease of the amount of batteries thrown out within one household. In the long run, this helps save many people spend less when purchasing batteries because they will almost never have to purchase batteries again (until the rechargeable battery wears out).
- Begin to transition from one-time plastic items to reuseable fiber, metal, wooden items.
  - Many people are beginning to convert their usage of one-time plastic bottles to reuseable metal bottles.
  - Many people are also beginning to transition from using one-time use plastic bag to resueable fiber bags. In particular, New York State has mandated a state-wide law on the banning on plastic bag. However, because this is a recent change (as of March 1, 2020), we have not been able to see the major effects that this law has put on the overall enviornment.
  - As a community, a shift in the common items that we use can prove to be extremely helpful towards the world that we live in.
Begin to sort out your garbage.
- Create separate areas for regular trash and recycled trash. This will allow for both you and your family members to make concious decisions on what they are putting into the trash can.
- Also, in New York City, most streets have multiple slots for which you can deposit your trash based on the type of trash that you are throwing out.
- Also, if you have any clothes or items that you either are not interested in or do not wear anymore, please remember to donate such items. Not only do they help someone in need, but they also allow for the reduction of waste for such items.

Thank you for viewing my project! If you have any further questions, please email Washieu Anan (wanan@browning.edu).

	Zone	District	Fiscal Month Number	Fiscal Year	Month Name	Diversion Rate-Total (Total Recycling / Total Waste)	Capture Rate-Paper (Total Paper / Max Paper)	Capture Rate-MGP (Total MGP / Max MGP)	Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100
0	Brooklyn North	BKN01	10	2019	April	14.7	44.9	43.0	44.1
1	Brooklyn North	BKN02	10	2019	April	20.0	34.2	57.9	41.2
2	Brooklyn North	BKN03	10	2019	April	12.2	33.5	44.9	38.2
3	Brooklyn North	BKN04	10	2019	April	15.5	35.2	68.5	48.8
4	Brooklyn North	BKN05	10	2019	April	10.1	22.3	45.1	31.5

	Fiscal Month Number	Fiscal Year	Diversion Rate-Total (Total Recycling / Total Waste)	Capture Rate-Paper (Total Paper / Max Paper)	Capture Rate-MGP (Total MGP / Max MGP)	Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100
count	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000
mean	6.500000	2017.500000	15.354167	48.291667	45.754167	47.129167
std	3.488583	1.129865	0.917202	4.008174	2.807131	2.605229
min	1.000000	2016.000000	13.500000	40.500000	39.900000	41.300000
25%	3.750000	2016.750000	14.700000	45.350000	43.925000	45.450000
50%	6.500000	2017.500000	15.350000	48.250000	46.050000	47.400000
75%	9.250000	2018.250000	16.025000	51.125000	47.125000	48.850000
max	12.000000	2019.000000	17.100000	58.400000	55.400000	52.900000

	Fiscal Month Number	Fiscal Year	Diversion Rate-Total (Total Recycling / Total Waste)	Capture Rate-Paper (Total Paper / Max Paper)	Capture Rate-MGP (Total MGP / Max MGP)	Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100
count	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000
mean	6.500000	2017.500000	19.262500	33.537500	56.170833	40.016667
std	3.488583	1.129865	1.489056	2.025752	3.015701	2.041832
min	1.000000	2016.000000	16.500000	28.200000	49.100000	34.400000
25%	3.750000	2016.750000	17.900000	32.400000	54.125000	38.850000
50%	6.500000	2017.500000	19.450000	33.550000	56.450000	40.000000
75%	9.250000	2018.250000	20.425000	34.550000	57.925000	41.200000
max	12.000000	2019.000000	22.500000	38.000000	63.800000	43.900000

	Fiscal Month Number	Fiscal Year	Diversion Rate-Total (Total Recycling / Total Waste)	Capture Rate-Paper (Total Paper / Max Paper)	Capture Rate-MGP (Total MGP / Max MGP)	Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100
count	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000
mean	6.500000	2017.500000	11.820833	34.677083	41.362500	37.472917
std	3.488583	1.129865	0.823836	3.078217	3.643743	2.744491
min	1.000000	2016.000000	10.100000	29.000000	32.800000	30.800000
25%	3.750000	2016.750000	11.300000	32.450000	39.525000	35.675000
50%	6.500000	2017.500000	11.800000	34.750000	41.400000	37.650000
75%	9.250000	2018.250000	12.325000	36.625000	43.775000	39.375000
max	12.000000	2019.000000	13.900000	41.800000	49.200000	42.900000

	Fiscal Month Number	Fiscal Year	Diversion Rate-Total (Total Recycling / Total Waste)	Capture Rate-Paper (Total Paper / Max Paper)	Capture Rate-MGP (Total MGP / Max MGP)	Capture Rate-Total ((Total Recycling - Leaves (Recycling)) / (Max Paper + Max MGP))x100
count	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000
mean	6.500000	2017.500000	14.685417	35.391667	61.737500	46.562500
std	3.488583	1.129865	1.127601	3.103041	6.137559	3.631636
min	1.000000	2016.000000	12.400000	30.300000	46.400000	37.800000
25%	3.750000	2016.750000	13.900000	32.500000	58.575000	44.075000
50%	6.500000	2017.500000	14.500000	35.850000	62.600000	47.150000
75%	9.250000	2018.250000	15.600000	37.525000	67.200000	49.275000
max	12.000000	2019.000000	17.100000	42.600000	73.800000	53.200000